nycflights13

Chapter 10: Tibbles

Read R4ds Chapter 10: Tibbles, sections 1-3.

10.1: Introduction

Load the tidyverse package.

library(tidyverse)

10.2: Creating tibbles

Enter your code chunks for Section 10.2 here.

as.tibble(iris)
tibble(
  x = 1:5, 
  y = 1, 
  z = x ^ 2 + y
)
tb <- tibble(
  `:)` = "smile", 
  ` ` = "space",
  `2000` = "number"
)
tb
tribble(
  ~x, ~y, ~z,
  #--|--|----
  "a", 2, 3.6,
  "b", 1, 8.5
)

Describe what each chunk code does. #1 Coeresed the data frame iris to tibble. #2 Made a tibble from a vector. #3 Creates a table with nonsynactic names #4 creats tibble with a very smal ammount of data ### 10.3: Tibbles vs data.frame

Enter your code chunks for Section 10.3 here. #3.2.1 #This tibble creates a table 1000 columns by 5 rows.

tibble(
  a = lubridate::now() + runif(1e3) * 86400,
  b = lubridate::today() + runif(1e3) * 30,
  c = 1:1e3,
  d = runif(1e3),
  e = sample(letters, 1e3, replace = TRUE)
)

#Cretes a table with infinite columns and 10 rows.

nycflights13::flights %>%
  print(n = 10, width = Inf)

#this gives you information about tibble.

package?tibble

#Opens a new tab with data.

nycflights13::flights %>%
  View()

#3.2.2 #this made a data frame from the variables

df <- tibble(x = runif(5), y = rnorm(5)) 

#extracts a variable

df$x
[1] 0.2726624 0.9823032 0.3205767 0.8882099 0.1346611

#This does the exact same thing as the code above.

df[["x"]]
[1] 0.2726624 0.9823032 0.3205767 0.8882099 0.1346611

#This extracts data by position.

df[[1]]
[1] 0.2726624 0.9823032 0.3205767 0.8882099 0.1346611

#Uses a placeholder in a pipe.

df %>% .$x
[1] 0.2726624 0.9823032 0.3205767 0.8882099 0.1346611

#This does the exact same thing as the code above.

df %>% .[["x"]]
[1] 0.2726624 0.9823032 0.3205767 0.8882099 0.1346611

10.4: Not required

#Thanks Dr. T #### Section 10.5 Questions

Answer the questions completely. Use code chunks, text, or both, as necessary.

1: How can you tell if an object is a tibble? (Hint: try printing mtcars, which is a regular data frame). Identify at least two ways to tell if an object is a tibble. Hint: What does as_tibble() do? What does class() do? What does str() do? #Using class or str will tell you if something is a tibble or not. There is also numbering of rows in a tibble but not in a data frame

mtcars
as_tibble(mtcars)
class(mtcars)
[1] "data.frame"
str(mtcars)
'data.frame':   32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

2: Compare and contrast the following operations on a data.frame and equivalent tibble. What is different? Why might the default data frame behaviours cause you frustration? #The data frame is easier to read, but has more code to write out. The tibble is less code to enter but a little harder to read.

df <- data.frame(abc = 1, xyz = "a")
df$x
[1] a
Levels: a
df[, "xyz"]
[1] a
Levels: a
df[, c("abc", "xyz")]
df <- tibble(abc = 1, xyz = "a")
df$abc
[1] 1

Chapter 11: Importing data

Read R4ds Chapter 11: Data Import, sections 1, 2, and 5.

11.1 Introduction

Nothing to do here unless you took a break and need to reload tidyverse.

11.2 Getting started.

Do not run the first code chunk of this section, which begins with heights <- read_csv("data/heights.csv"). You do not have that data file so the code will not run.

Enter and run the remaining chunks in this section.

read_csv("a,b,c
1,2,3
4,5,6")
read_csv("The first line of metadata
         The second line of metadata
         x,y,z
         1,2,3", skip = 2)
read_csv("# A comment I want to skip
         x,y,z
         1,2,3", comment = "#")
read_csv("1,2,3\n4,5,6", col_names = FALSE)
read_csv("1,2,3\n4,5,6", col_names = c("x", "y", "z"))
read_csv("a,b,c\n1,2,.", na = ".")

11.2 Questions

1: What function would you use to read a file where fields were separated with “|”? #You should use read_delin() because that can read files with delimiters.

2: (This question is modified from the text.) Finish the two lines of read_delim code so that the first one would read a comma-separated file and the second would read a tab-separated file. You only need to worry about the delimiter. Do not worry about other arguments. Replace the dots in each line with the rest of your code.

Comma-separated

file <- read_delim("file.csv", ...)


`file <- read_delim("file.csv", delim = ",")```
Error: attempt to use zero-length variable name
read_csv("a,b\n1,2,3\n4,5,6")
2 parsing failures.
row col  expected    actual         file
  1  -- 2 columns 3 columns literal data
  2  -- 2 columns 3 columns literal data
read_csv("a,b,c\n1,2\n1,2,3,4")
2 parsing failures.
row col  expected    actual         file
  1  -- 3 columns 2 columns literal data
  2  -- 3 columns 4 columns literal data
read_csv("a,b\n\"1")
2 parsing failures.
row col                     expected    actual         file
  1  a  closing quote at end of file           literal data
  1  -- 2 columns                    1 columns literal data
read_csv("a,b\n1,2\na,b")
read_csv("a;b\n1;3")

3: What are the two most important arguments to read_fwf()? Why? #col_position and col_types becauae with these you can name as well as specify what goes in each column.

4: Skip this question

5: Identify what is wrong with each of the following inline CSV files. What happens when you run the code?

table4a

#The first one codes for 2 columns but has enough code for three columns. The next code is set up for three columns but only has info fo two. The next code was set up for two columns but has enough code for one column. The next has too much listed as headers. the last one is seperated by semicolons when it should be seperated by commas. ### 11.3 and 11.4: Not required

11.5: Writing to a file

Just read this section. You may find it helpful in the future to save a data file to your hard drive. It is basically the same format as reading a file, except that you must specify the data object to save, in addition to the path and file name.

11.6 Not required

Chapter 18: Pipes

Read R4ds Chapter 18: Pipes, sections 1-3.

Nothing to do otherwise for this chapter. Is this easy or what?

Note: Trying using pipes for all of the remaining examples. That will help you understand them.

Chapter 12: Tidy Data

Read R4ds Chapter 12: Tidy Data, sections 1-3, 7.

12.1 Introduction

Nothing to do here unless you took a break and need to reload the tidyverse.

12.2 Tidy data

Study Figure 12.1 and relate the diagram to the three rules listed just above them. Relate that back to the example I gave you in the notes. Bear this in mind as you make data tidy in the second part of this assignment.

You do not have to run any of the examples in this section.

12.3

Read and run the examples through section 12.3.1 (gathering), including the example with left_join(). We’ll cover joins later.


table4a %>% 
  gather(`1999`, `2000`, key = "year", value = "cases")

table4b %>% 
  gather(`1999`, `2000`, key = "year", value = "population")
tidy4a <- table4a %>% 
  gather(`1999`, `2000`, key = "year", value = "cases")
tidy4b <- table4b %>% 
  gather(`1999`, `2000`, key = "year", value = "population")
left_join(tidy4a, tidy4b)
Joining, by = c("country", "year")
table2
table2 %>%
    spread(key = type, value = count)
table2 %>%
    spread(key = type, value = count)

12.3 Questions

2: Why does this code fail? Fix it so it works.

table4a %>% 
  gather(`1999`, `2000`, key = "year", value = "cases")
table4a %>% 
  gather(`1999`, `2000`, key = "year", value = "cases")

#There needs to be ticks around the years. That is all for Chapter 12. On to the last chapter.

Chapter 5: Data transformation

Read R4ds Chapter 5: Data Transformation, sections 1-4.

Time to get small.

5.1: Introduction

Load the necessary libraries. As usual, type the examples into and run the code chunks.

library(tidyverse)

nycflights13::flights
flights
filter(flights, month == 1, day == 1)
filter(flights, month == 1, day == 1)
(dec25 <- filter(flights, month == 12, day == 25)))
Error: unexpected ')' in "(dec25 <- filter(flights, month == 12, day == 25)))"
filter(flights, month == 1)
sqrt(2) ^ 2 == 2
[1] FALSE
filter(flights, month == 11 | month == 12)

5.2: Filter rows with filter()

Study Figure 5.1 carefully. Once you learn the &, |, and ! logic, you will find them to be very powerful tools.

nov_dec <- filter(flights, month %in% c(11, 12))
filter(flights, !(arr_delay > 120 | dep_delay > 120))
filter(flights, arr_delay <= 120, dep_delay <= 120)
NA > 5
[1] NA
10 == NA
[1] NA
NA + 10
[1] NA
NA / 2
[1] NA
NA == NA
[1] NA
x <- NA
y <- NA
x == y
[1] NA
is.na(x)
[1] TRUE
df <- tibble(x = c(1, NA, 3))
filter(df, x > 1)
filter(df, is.na(x) | x > 1)
filter(flights, (arr_delay > 120 | dep_delay > 120))

5.2 Questions

1.1: Find all flights with a delay of 2 hours or more.

filter(flights, dest == 'IAH' | dest == 'HOU')

1.2: Flew to Houston (IAH or HOU)

filter(flights, carrier == 'UA' | carrier == 'AA' | carrier == 'DL')

1.3: Were operated by United (UA), American (AA), or Delta (DL).

filter(flights, month >= 7 & month <= 9)

1.4: Departed in summer (July, August, and September).

filter(flights, arr_delay > 120, dep_delay <= 0)

1.5: Arrived more than two hours late, but didn’t leave late.

filter(flights, dep_delay >= 60, dep_delay-arr_delay > 30)

1.6: Were delayed by at least an hour, but made up over 30 minutes in flight. This is a tricky one. Do your best.

filter(flights, dep_time <=600 | dep_time == 2400)

1.7: Departed between midnight and 6am (inclusive)

filter(flights, between(month, 7, 9))
filter(flights, !between(dep_time, 601, 2359))

2: Another useful dplyr filtering helper is between(). What does it do? Can you use it to simplify the code needed to answer the previous challenges?

summary(flights)
      year          month       
 Min.   :2013   Min.   : 1.000  
 1st Qu.:2013   1st Qu.: 4.000  
 Median :2013   Median : 7.000  
 Mean   :2013   Mean   : 6.549  
 3rd Qu.:2013   3rd Qu.:10.000  
 Max.   :2013   Max.   :12.000  
                                
      day           dep_time   
 Min.   : 1.00   Min.   :   1  
 1st Qu.: 8.00   1st Qu.: 907  
 Median :16.00   Median :1401  
 Mean   :15.71   Mean   :1349  
 3rd Qu.:23.00   3rd Qu.:1744  
 Max.   :31.00   Max.   :2400  
                 NA's   :8255  
 sched_dep_time   dep_delay      
 Min.   : 106   Min.   : -43.00  
 1st Qu.: 906   1st Qu.:  -5.00  
 Median :1359   Median :  -2.00  
 Mean   :1344   Mean   :  12.64  
 3rd Qu.:1729   3rd Qu.:  11.00  
 Max.   :2359   Max.   :1301.00  
                NA's   :8255     
    arr_time    sched_arr_time
 Min.   :   1   Min.   :   1  
 1st Qu.:1104   1st Qu.:1124  
 Median :1535   Median :1556  
 Mean   :1502   Mean   :1536  
 3rd Qu.:1940   3rd Qu.:1945  
 Max.   :2400   Max.   :2359  
 NA's   :8713                 
   arr_delay          carrier         
 Min.   : -86.000   Length:336776     
 1st Qu.: -17.000   Class :character  
 Median :  -5.000   Mode  :character  
 Mean   :   6.895                     
 3rd Qu.:  14.000                     
 Max.   :1272.000                     
 NA's   :9430                         
     flight       tailnum         
 Min.   :   1   Length:336776     
 1st Qu.: 553   Class :character  
 Median :1496   Mode  :character  
 Mean   :1972                     
 3rd Qu.:3465                     
 Max.   :8500                     
                                  
    origin              dest          
 Length:336776      Length:336776     
 Class :character   Class :character  
 Mode  :character   Mode  :character  
                                      
                                      
                                      
                                      
    air_time        distance   
 Min.   : 20.0   Min.   :  17  
 1st Qu.: 82.0   1st Qu.: 502  
 Median :129.0   Median : 872  
 Mean   :150.7   Mean   :1040  
 3rd Qu.:192.0   3rd Qu.:1389  
 Max.   :695.0   Max.   :4983  
 NA's   :9430                  
      hour           minute     
 Min.   : 1.00   Min.   : 0.00  
 1st Qu.: 9.00   1st Qu.: 8.00  
 Median :13.00   Median :29.00  
 Mean   :13.18   Mean   :26.23  
 3rd Qu.:17.00   3rd Qu.:44.00  
 Max.   :23.00   Max.   :59.00  
                                
   time_hour                  
 Min.   :2013-01-01 05:00:00  
 1st Qu.:2013-04-04 13:00:00  
 Median :2013-07-03 10:00:00  
 Mean   :2013-07-03 05:22:54  
 3rd Qu.:2013-10-01 07:00:00  
 Max.   :2013-12-31 23:00:00  
                              

#This code pulls the data from the first varible listed and the last lsited. It makes the previous two questins much easier. 3: How many flights have a missing dep_time? What other variables are missing? What might these rows represent?

NA ^ 0
[1] 1
NA | TRUE
[1] TRUE
FALSE & NA
[1] FALSE

8255= missing dep_time + dep_delay. 8713= missing arr_time. 9430=missing arr_delay. 9430= missing air_time. 4: Why is NA ^ 0 not missing? Why is NA | TRUE not missing? Why is FALSE & NA not missing? Can you figure out the general rule? (NA * 0 is a tricky counterexample!)

arrange(flights, year, month, day)

Note: For some context, see this thread

5.3 Arrange with arrange()

arrange(flights, desc(dep_delay))
df <- tibble(x = c(5, 2, NA))
arrange(df, x)
arrange(df, desc(x))
arrange(df, desc(is.na(x)))

5.3 Questions

1: How could you use arrange() to sort all missing values to the start? (Hint: use is.na()). Note: This one should still have the earliest departure dates after the NAs. Hint: What does desc() do?

arrange(flights, desc(dep_delay))
arrange(flights, dep_delay)

2: Sort flights to find the most delayed flights. Find the flights that left earliest.

This question is asking for the flights that were most delayed (left latest after scheduled departure time) and least delayed (left ahead of scheduled time).

arrange(flights, air_time)

3: Sort flights to find the fastest flights. Interpret fastest to mean shortest time in the air.

arrange(flights, distance/hour)

Optional challenge: fastest flight could refer to fastest air speed. Speed is measured in miles per hour but time is minutes. Arrange the data by fastest air speed.

arrange(flights, distance)

4: Which flights travelled the longest? Which travelled the shortest

arrange(flights, desc(distance))
arrange(flights, desc(distance))

5.4 Select columns with select()

select(flights, year, month, day)
select(flights, year:day)
select(flights, -(year:day))
rename(flights, departuretime = dep_time)
?select
vars <- c("dep_time", "dep_delay", "arr_time", "arr_delay")
select(flights, starts_with("dep"), starts_with("arr"))
select(flights, one_of(vars))
select(flights, dep_time, dep_delay, arr_time, arr_delay)
NA

5.4 Questions

1: Brainstorm as many ways as possible to select dep_time, dep_delay, arr_time, and arr_delay from flights. Find at least three ways.

select(flights, dest, origin, dest, dest)

2: What happens if you include the name of a variable multiple times in a select() call?

vars <- c("year", "month", "day", "dep_delay", "arr_delay")
select(flights, one_of(vars))

#There doesnt seem to be any problem with this. 3: What does the one_of() function do? Why might it be helpful in conjunction with this vector?

vars <- c("year", "month", "day", "dep_delay", "arr_delay")

vars <- c("year", "month", "day", "dep_delay", "arr_delay")
select(flights, one_of(vars))

#This function only uses the variables in the vector.

4: Does the result of running the following code surprise you? How do the select helpers deal with case by default? How can you change that default?

select(flights, contains("TIME"))

select(flights, contains("TIME", ignore.case = TRUE))

#This picks out all variables with the indicated word or phrase.

---
title: "HW05 Part 1: Complete the sections"
author: "Morgan Tackett"
date: "`r format(Sys.time(), '%d %B %Y')`"
output: html_notebook
editor_options: 
  chunk_output_type: inline
---
```{r}
nycflights13
```

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

- Change "your name" in the YAML header above to your name.

- As usual, enter the examples in code chunks and run them, unless told otherwise.

## Chapter 10: Tibbles

Read [R4ds Chapter 10: Tibbles](https://r4ds.had.co.nz/tibbles.html), sections 1-3.

### 10.1: Introduction

Load the tidyverse package. 
```{r}
library(tidyverse)
```

### 10.2: Creating tibbles

Enter your code chunks for Section 10.2 here.
```{r}
as.tibble(iris)
```
```{r}
tibble(
  x = 1:5, 
  y = 1, 
  z = x ^ 2 + y
)
```
```{r}
tb <- tibble(
  `:)` = "smile", 
  ` ` = "space",
  `2000` = "number"
)
tb
```
```{r}
tribble(
  ~x, ~y, ~z,
  #--|--|----
  "a", 2, 3.6,
  "b", 1, 8.5
)
```

Describe what each chunk code does. 
#1 Coeresed the data frame iris to tibble. 
#2 Made a tibble from a vector. 
#3 Creates a table with nonsynactic names
#4 creats tibble with a very smal ammount of data
### 10.3: Tibbles vs data.frame

Enter your code chunks for Section 10.3 here.
#3.2.1
#This tibble creates a table 1000 columns by 5 rows.
```{r}
tibble(
  a = lubridate::now() + runif(1e3) * 86400,
  b = lubridate::today() + runif(1e3) * 30,
  c = 1:1e3,
  d = runif(1e3),
  e = sample(letters, 1e3, replace = TRUE)
)
```
#Cretes a table with infinite columns and 10 rows. 
```{r}
nycflights13::flights %>%
  print(n = 10, width = Inf)
```
#this gives you information about tibble. 
```{r}
package?tibble
```
#Opens a new tab with data.
```{r}
nycflights13::flights %>%
  View()
```

#3.2.2
#this made a data frame from the variables
```{r}
df <- tibble(x = runif(5), y = rnorm(5)) 
```
#extracts a variable
```{r}
df$x
```
#This does the exact same thing as the code above.
```{r}
df[["x"]]
```
#This extracts data by position.
```{r}
df[[1]]
```
#Uses a placeholder in a pipe.
```{r}
df %>% .$x
```
#This does the exact same thing as the code above. 
```{r}
df %>% .[["x"]]
```


### 10.4: Not required
#Thanks Dr. T
#### Section 10.5 Questions

Answer the questions *completely.* Use code chunks, text, or both, as necessary.

**1:** How can you tell if an object is a tibble? (Hint: try printing `mtcars`, which is a regular data frame). Identify at least two ways to tell if an object is a tibble. *Hint:* What does `as_tibble()` do? What does `class()` do? What does `str()` do?
#Using class or str will tell you if something is a tibble or not. There is also numbering of rows in a tibble but not in a data frame

```{r}
mtcars
```
```{r}
as_tibble(mtcars)
```
```{r}
class(mtcars)
```
```{r}
str(mtcars)
```


**2:** Compare and contrast the following operations on a data.frame and equivalent tibble. What is different? Why might the default data frame behaviours cause you frustration?
#The data frame is easier to read, but has more code to write out. The tibble is less code to enter but a little harder to read. 
```{r}
df <- data.frame(abc = 1, xyz = "a")
df$x
df[, "xyz"]
df[, c("abc", "xyz")]
```
```{r}
df <- tibble(abc = 1, xyz = "a")
df$abc
```


## Chapter 11: Importing data

Read [R4ds Chapter 11: Data Import](https://r4ds.had.co.nz/data-import.html), sections 1, 2, and 5.

### 11.1 Introduction

Nothing to do here unless you took a break and need to reload `tidyverse`.

### 11.2 Getting started.

Do *not* run the first code chunk of this section, which begins with `heights <- read_csv("data/heights.csv")`. You do not have that data file so the code will not run.

Enter and run the remaining chunks in this section.

```{r}
read_csv("a,b,c
1,2,3
4,5,6")
```
```{r}
read_csv("The first line of metadata
         The second line of metadata
         x,y,z
         1,2,3", skip = 2)
```
```{r}
read_csv("# A comment I want to skip
         x,y,z
         1,2,3", comment = "#")
```
```{r}
read_csv("1,2,3\n4,5,6", col_names = FALSE)
```
```{r}
read_csv("1,2,3\n4,5,6", col_names = c("x", "y", "z"))
```
```{r}
read_csv("a,b,c\n1,2,.", na = ".")
```





#### 11.2 Questions

**1:** What function would you use to read a file where fields were separated with "|"?
#You should use read_delin() because that can read files with delimiters.


**2:** (This question is modified from the text.) Finish the two lines of `read_delim` code so that the first one would read a comma-separated file and the second would read a tab-separated file. You only need to worry about the delimiter. Do not worry about other arguments. Replace the dots in each line with the rest of your code. 

# Comma-separated
`file <- read_delim("file.csv", ...)`
```{r}

`file <- read_delim("file.csv", delim = ",")```


# Tab-separated
`file <- read_delim("file.csv", ...)`
```{r}
`file <- read_delim("file.csv", delim = "\t")
```


**3:** What are the two most important arguments to `read_fwf()`? Why?
#col_position and col_types becauae with these you can name as well as specify what goes in each column. 

**4:** Skip this question


**5: ** Identify what is wrong with each of the following inline CSV files. What happens when you run the code?

```{r}
read_csv("a,b\n1,2,3\n4,5,6")
read_csv("a,b,c\n1,2\n1,2,3,4")
read_csv("a,b\n\"1")
read_csv("a,b\n1,2\na,b")
read_csv("a;b\n1;3")
```
#The first one codes for 2 columns but has enough code for three columns. The next code is set up for three columns but only has info fo two. The next code was set up for two columns but has enough code for one column. The next has too much listed as headers. the last one is seperated by semicolons when it should be seperated by commas. 
### 11.3 and 11.4: Not required

### 11.5: Writing to a file

Just read this section. You may find it helpful in the future to save a data file to your hard drive. It is basically the same format as reading a file, except that you must specify the data object to save, in addition to the path and file name.

### 11.6 Not required

## Chapter 18: Pipes

Read [R4ds Chapter 18: Pipes](https://r4ds.had.co.nz/pipes.html), sections 1-3.

Nothing to do otherwise for this chapter. Is this easy or what?

**Note:** Trying using pipes for all of the remaining examples. That will help you understand them.

## Chapter 12: Tidy Data

Read [R4ds Chapter 12: Tidy Data](https://r4ds.had.co.nz/tidy-data.html), sections 1-3, 7. 

### 12.1 Introduction

Nothing to do here unless you took a break and need to reload the `tidyverse.`

### 12.2 Tidy data

Study Figure 12.1 and relate the diagram to the three rules listed just above them. Relate that back to the example I gave you in the notes. Bear this in mind as you make data tidy in the second part of this assignment.

You do not have to run any of the examples in this section.

### 12.3

Read and run the examples through section 12.3.1 (gathering), including the example with `left_join()`. We'll cover joins later.
```{r}
table4a
```
```{r}

table4a %>% 
  gather(`1999`, `2000`, key = "year", value = "cases")
```
```{r}

table4b %>% 
  gather(`1999`, `2000`, key = "year", value = "population")
```
```{r}
tidy4a <- table4a %>% 
  gather(`1999`, `2000`, key = "year", value = "cases")
tidy4b <- table4b %>% 
  gather(`1999`, `2000`, key = "year", value = "population")
left_join(tidy4a, tidy4b)
```
```{r}
table2
```
```{r}
table2 %>%
    spread(key = type, value = count)
```

#### 12.3 Questions

**2:** Why does this code fail? Fix it so it works.

```{r}
table4a %>% 
  gather(1999, 2000, key = "year", value = "cases")
#> Error in inds_combine(.vars, ind_list): Position must be between 0 and n
```
```{r}
table4a %>% 
  gather(`1999`, `2000`, key = "year", value = "cases")
```
#There needs to be ticks around the years. 
That is all for Chapter 12. On to the last chapter.


## Chapter 5: Data transformation

Read [R4ds Chapter 5: Data Transformation](https://r4ds.had.co.nz/transform.html), sections 1-4.

Time to [get small.](https://www.youtube.com/watch?v=GOrdzCHnpw4) 

### 5.1: Introduction

Load the necessary libraries. As usual, type the examples into and run the code chunks.
```{r}
library(nycflights13)
```


```{r}
library(tidyverse)
```
```{r}

nycflights13::flights
```
```{r}
flights
```
```{r}
filter(flights, month == 1, day == 1)
```
```{r}
```


```{r}
jan1 <- filter(flights, month == 1, day == 1)
```
```{r}
(dec25 <- filter(flights, month == 12, day == 25)))
```
```{r}
filter(flights, month == 1)
```
```{r}

```

```{r}
near(sqrt(2) ^ 2,  2)
near(1 / 49 * 49, 1)
```


### 5.2: Filter rows with `filter()`

Study Figure 5.1 carefully. Once you learn the `&`, `|`, and `!` logic, you will find them to be very powerful tools.

```{r}
filter(flights, month == 11 | month == 12)
```

```{r}
nov_dec <- filter(flights, month %in% c(11, 12))
```

```{r}
filter(flights, !(arr_delay > 120 | dep_delay > 120))
filter(flights, arr_delay <= 120, dep_delay <= 120)
```

```{r}
NA > 5
10 == NA
NA + 10
NA / 2
```
```{r}
NA == NA
```

```{r}
x <- NA
y <- NA
x == y
```

```{r}
is.na(x)
```

```{r}
df <- tibble(x = c(1, NA, 3))
filter(df, x > 1)
filter(df, is.na(x) | x > 1)
```

#### 5.2 Questions

**1.1:** Find all flights with a delay of 2 hours or more.

```{r}
filter(flights, (arr_delay > 120 | dep_delay > 120))
```

**1.2:** Flew to Houston (IAH or HOU)
```{r}
filter(flights, dest == 'IAH' | dest == 'HOU')
```

**1.3:** Were operated by United (UA), American (AA), or Delta (DL).
```{r}
filter(flights, carrier == 'UA' | carrier == 'AA' | carrier == 'DL')
```

**1.4:** Departed in summer (July, August, and September).
```{r}
filter(flights, month >= 7 & month <= 9)
```

**1.5:** Arrived more than two hours late, but didn’t leave late.
```{r}
filter(flights, arr_delay > 120, dep_delay <= 0)
```

**1.6:** Were delayed by at least an hour, but made up over 30 minutes in flight. This is a tricky one. Do your best.
```{r}
filter(flights, dep_delay >= 60, dep_delay-arr_delay > 30)
```


**1.7:** Departed between midnight and 6am (inclusive)
```{r}
filter(flights, dep_time <=600 | dep_time == 2400)
```

**2:** Another useful dplyr filtering helper is `between()`. What does it do? Can you use it to simplify the code needed to answer the previous challenges?

```{r}
filter(flights, between(month, 7, 9))
filter(flights, !between(dep_time, 601, 2359))
```
#This code pulls the data from the first varible listed and the last lsited. It makes the previous two questins much easier.
**3:** How many flights have a missing dep_time? What other variables are missing? What might these rows represent?
```{r}
summary(flights)
```
8255= missing dep_time + dep_delay. 8713= missing arr_time. 9430=missing arr_delay. 
9430= missing air_time.
**4:** Why is `NA ^ 0` not missing? Why is `NA | TRUE` not missing? Why is `FALSE & NA` not missing? Can you figure out the general rule? (`NA * 0` is a tricky counterexample!)
```{r}
NA ^ 0
NA | TRUE
FALSE & NA
```
**Note:** For some context, see [this thread](https://blog.revolutionanalytics.com/2016/07/understanding-na-in-r.html)


### 5.3 Arrange with `arrange()`

```{r}
arrange(flights, year, month, day)
```

```{r}
arrange(flights, desc(dep_delay))
```

```{r}
df <- tibble(x = c(5, 2, NA))
arrange(df, x)
```

```{r}
arrange(df, desc(x))
```

#### 5.3 Questions

**1:** How could you use `arrange()` to sort all missing values to the start? (Hint: use is.na()). **Note:** This one should still have the earliest departure dates after the `NA`s. *Hint:* What does `desc()` do?

```{r}
arrange(df, desc(is.na(x)))
```
**2:** Sort flights to find the most delayed flights. Find the flights that left earliest. 

This question is asking for the flights that were most delayed (left latest after scheduled departure time) and least delayed (left ahead of scheduled time).

```{r}
arrange(flights, desc(dep_delay))
arrange(flights, dep_delay)
```
**3:** Sort flights to find the fastest flights. Interpret fastest to mean shortest time in the air.
```{r}
arrange(flights, air_time)
```


*Optional challenge:* fastest flight could refer to fastest air speed. Speed is measured in miles per hour but time is minutes. Arrange the data by fastest air speed.
```{r}
arrange(flights, distance/hour)
```


**4:** Which flights travelled the longest? Which travelled the shortest
```{r}
arrange(flights, distance)
```

```{r}
arrange(flights, desc(distance))
```

### 5.4 Select columns with `select()`
```{r}
select(flights, year, month, day)
```

```{r}
select(flights, year:day)
```

```{r}
select(flights, -(year:day))
```

```{r}
rename(flights, departuretime = dep_time)
```

```{r}
select(flights, carrier, dest, everything())
```
#### 5.4 Questions

**1:** Brainstorm as many ways as possible to select `dep_time`, `dep_delay`, `arr_time`, and `arr_delay` from flights. Find at least three ways.
```{r}
?select
vars <- c("dep_time", "dep_delay", "arr_time", "arr_delay")
select(flights, starts_with("dep"), starts_with("arr"))
select(flights, one_of(vars))
select(flights, dep_time, dep_delay, arr_time, arr_delay)

```

**2:** What happens if you include the name of a variable multiple times in a `select()` call?

```{r}
select(flights, dest, origin, dest, dest)
```
#There doesnt seem to be any problem with this.
**3:** What does the `one_of()` function do? Why might it be helpful in conjunction with this vector?

`vars <- c("year", "month", "day", "dep_delay", "arr_delay")`

```{r}
vars <- c("year", "month", "day", "dep_delay", "arr_delay")
select(flights, one_of(vars))
```
#This function only uses the variables in the vector. 

**4:** Does the result of running the following code surprise you? How do the select helpers deal with case by default? How can you change that default?

`select(flights, contains("TIME"))`

```{r}
select(flights, contains("TIME", ignore.case = TRUE))
```
#This picks out all variables with the indicated word or phrase. 

